Selective Image Compression Using MSIC Algorithm

This paper presents a new algorithm, Magnitude Sensitive Image Compression (MSIC), as a reliable and efficient approach for selective image compression. The algorithm uses MSCL neural networks (in direct and masked versions). These kind of neural networks tend to focus the learning process in data space zones with high values of a user-defined magnitude function. This property can be used for image compression to divide the image in irregular blocks, with higher resolution in areas of interest. These blocks are compressed by Vector Quantization in a later step, giving as a result that different areas of the image receive distinct compression ratios. Results in several examples demonstrate the better performance of MSIC compared to JPEG or other SOM based image compression algorithms.